datamodel-code-generator VS fastapi

Like generating pydantic models or dataclasses for an OpenAPI schema? I haven't needed to go in that direction myself, but this[0] looks promising!

Apologies if I've misunderstood your comment

https://koxudaxi.github.io/datamodel-code-generator/

I'm sorry, but you have completely misunderstood the purpose of Open API.

It is not a specification to define your business logic classes and objects -- either client or server side. Its goal is to define the interface of an API, and to provide a single source of truth that requests and responses can be validated against. It contains everything you need to know to make requests to an API; code generation is nice to have (and I use it myself, but mainly on the server side, for routing and validation), but not something required or expected from OpenAPI

For what it's worth, my personal preferred workflow to build an API is as follows:

1. Build the OpenAPI spec first. A smaller spec could easily be done by hand, but I prefer using a design tool like Stoplight [0]; it has the best Web-based OpenAPI (and JSON Schema) editor I have encountered, and integrates with git nearly flawlessly.

2. Use an automated tool to generate the API code implementation. Again, a static generation tool such as datamodel-code-generator [1] (which generates Pydantic models) would suffice, but for Python I prefer the dynamic request routing and validation provided by pyapi-server [2].

3. Finally, I use automated testing tools such as schemathesis [3] to test the implementation against the specification.

[0] https://stoplight.io/

[1] https://koxudaxi.github.io/datamodel-code-generator/

[2] https://pyapi-server.readthedocs.io

[3] https://schemathesis.readthedocs.io

The site also provide a link to the github repo of the underlying program.

Not sure who owns/hosts the site, but its based on this github repo.

That's what datamodel-code-generator propose.

had generally good experience creating typed wrappers for api's with json-schema-to-pydantic[0] converter

[0] https://github.com/koxudaxi/datamodel-code-generator

To save you some work, if you have already an OpenAPI specification at hand, you can use datamodel-code-generator to generate your Pydantic models from the spec.

And there are code generators for it! https://github.com/koxudaxi/datamodel-code-generator/

An API application framework (such as FastAPI)

It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.

This ends up made worse if you’re using sync routes.

If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.

This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759

Fast API.

We will create our API using FastAPI, a modern high-performance web framework for building fast APIs with Python. It is designed to be easy to use, efficient, and highly scalable. Some key features of FastAPI include:

FastAPI is a modern, fast, web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use, fast to run, and secure. In this blog post, we’ll explore the key features of FastAPI and walk through the process of creating a simple API using this powerful framework.

FastAPI is a modern, fast web framework for building APIs with Python 3.7+ that automatically generates OpenAPI and JSON Schema documentation. While FastAPI simplifies API development, manually creating and updating API documentation can still be a time-consuming task. In this blog post, we’ll explore how to leverage FastAPI’s automatic documentation generation capabilities, specifically focusing on Swagger and ReDoc, and how to streamline the process of documenting your APIs.

TiTiler is a dynamic tile server built on FastAPI and Rasterio/GDAL. Its main features include support for Cloud Optimized GeoTIFF(COG), multiple projection methods, various output formats (JPEG, JP2, PNG, WEBP, GTIFF, NumpyTile), WMTS, and virtual mosaic. It also provides Lambda and ECS deployment environments using AWS CDK.

To make it a bit more realistic, we’re going to use a FastAPI route as an example, and we’re also going to use FastAPI’s dependency injection, which can really help with readability (and testability, but more on that later).

2. FastAPI - Turbocharge Your Web APIs with Python ⚡